For techniques to convert 2D images to 3D images, the available methods are: 1) selecting 2D images; 2) analyzing the selected 2D images; 3) creating an automatic depth map using a conversion engine; 4) if the automatic depth map is a bad depth map, adjusting the following parameters: Depth-Scene, Perspective Setting, High-Dynamic-Range (HDR), Depth Level, Zero Plane Position, Depth Refine, Roto the mass; 5) manually adjusting the depth value of the mass and the edge; and 6) creating a new depth map.
Some known techniques to convert 2D images to 3D images have a conversion engine that use contrast and edge of color to perform conversion. In a depth map, depths of different image blocks depend on the target distance. Since the foreground is close to the observer, it can be brighter and nearly white. The background can be nearly black.
These available techniques cause the following limitations:
a) If the color of background is similar to the color of the major target, the conversion engine can assign wrong depth values for the major target and background. For example, for a man standing on a white ground with a pair of white shoes, the conversion engine can assign the white shoes and the white ground as the same layer and the same depth for processing.
b) Parameter adjustment to refine depth map is required after a depth map is created by the conversion engine. This parameter adjustment procedure is completed manually, and must be repeated every time a similar 2D image is converted. Thus, the procedure is time-consuming.
c) Also, conversion procedure must be performed for every single frame in a video. Further, the depth map cannot be reused for similar frames.
Thus, there is a need for a more efficient and accurate method and system to improve the quality of 2D-to-3D conversion.